SOTAVerified

Retrieval

A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.

Papers

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Showing 5276–5300 of 14297 papers

TitleStatusHype
AskMe: A LAPPS Grid-based NLP Query and Retrieval System for Covid-19 Literature—0
AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method—0
Global–Local Information Soft-Alignment for Cross-Modal Remote-Sensing Image–Text Retrieval—0
Fast Metric Learning For Deep Neural Networks—0
Fast Loop Closure Detection via Binary Content—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Fine-grained Retrieval Prompt Tuning—0
Fine-Grained Sentence Functions for Short-Text Conversation—0
Fast Kronecker product kernel methods via generalized vec trick—0
Fine-grained Text-Video Retrieval with Frozen Image Encoders—0
CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval—0
Asking More Informative Questions for Grounded Retrieval—0
Global Ranking Using Continuous Conditional Random Fields—0
Fast Interactive Image Retrieval using large-scale unlabeled data—0
CLAREL: Classification via retrieval loss for zero-shot learning—0
FastHybrid: A Hybrid Model for Efficient Answer Selection—0
Fast Gumbel-Max Sketch and its Applications—0
Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets—0
Fine-tuning and aligning question answering models for complex information extraction tasks—0
Fast Global Convergence for Low-rank Matrix Recovery via Riemannian Gradient Descent with Random Initialization—0
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing—0
COIN: Co-Cluster Infomax for Bipartite Graphs—0
Fine-tuning Language Models for Factuality—0
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes—0
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms—0
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All datasetsQuora Question PairsHotpotQANatural QuestionsOK-VQAInfoSeekMVKPolyvorePubMedQAPubMedQA corpus with metadataToolLensคลิปไวรัล!! ไอซ์ ปรีชญา ลืมปิดไลฟ์สดตอนอาบน้ำ ถูกแชร์กระหึ่มเน็ต

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second183.53—Unverified
2ElasticsearchQueries per second21.8—Unverified
3BM25-PTQueries per second6.49—Unverified
4Rank-BM25Queries per second1.18—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second20.88—Unverified
2ElasticsearchQueries per second7.11—Unverified
3Rank-BM25Queries per second0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second41.85—Unverified
2ElasticsearchQueries per second12.16—Unverified
3Rank-BM25Queries per second0.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FLMRRecall@589.32—Unverified
2RA-VQARecall@582.84—Unverified
#ModelMetricClaimedVerifiedStatus
1PreFLMRRecall@562.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP-KIStext-to-video Mean Rank30—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP4OutfitRecall@57.59—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1COLTCOMP@84.55—Unverified
#ModelMetricClaimedVerifiedStatus
1hello0L1,121,222—Unverified